DETAILED ACTION
This communication is in response to the Application filed on 24 December 2024. Claim 1 is pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for enabling topic-based verbal interaction with a virtual assistant. The limitations of claim 1 is for detecting key segments in audio or video, as drafted, describe a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations of claim 1 for enabling topic-based verbal interaction with a virtual assistant, as drafted, are a computer program product or apparatus that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than “instructions” “computer”, “processor”, and “memory” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer hardware language, claim 1 and the other independent claims encompass steps than may be performed manually by the user. Specifically, a first person (i.e., an observer) could listen to the spoken query of a second person and listen to a third person’s reply to that query. The observer could then determine that further audio to be spoken (say, by the second person) comprises an indication that a response to the query should not have been output. The observer may then communicate to the third person that a response to the query should not have been output, thereby decreasing the likelihood of the third person providing a response to this query at a future time. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the independent claims only recite the additional elements “computer readable storage medium”, “instructions” “computer”, “processor”, and “memory” to perform the aforementioned steps. The processor and other hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function for computer-based systematic literature review such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional hardware elements to perform both the aforementioned steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Allowable Subject Matter
Claim 1 is allowed, if the 101 rejection is remedied. The closest prior art of record includes US 9679568 (Taubman et al.), US 20180367480 (Housman), US 20180225365 (Altaf et al.), and US 20180189267 (Takiel). Taubman et al., col. 18, lines 39-54, discloses a method for training a dialog system using user feedback. According to one implementation, a method includes receiving, by a dialog engine, a first input that specifies a question; providing, by the dialog engine, an answer to the question; receiving, by the dialog engine, a second input; and determining, by the dialog engine, that the second input is classified as feedback to the answer, then determining a feedback score associated with the second input. In some implementations, the system may determine the predetermined feedback score is higher than a threshold, and classify the feedback as positive feedback, where the confidence score may be adjusted higher based on classifying the feedback as positive feedback. For example, the user device may increase the previous question-answer pair score based on the feedback score.
Houssman, para [0084], discloses performing topic modeling on the conversation based on the determined subject matters of the incoming and outgoing messages to determine a subject matter of the conversation. The determined subject matter of the conversation may be used to direct or steer the conversation towards the desired outcome. Accordingly, the topic module may use the conversation topic as input to the machine learning model, in addition to other features described above, to predict responses to the incoming message that increase the likelihood that the outgoing message will result in the desired outcome.
Altaf et al., para [0062], discloses wherein a plurality of matches of query topics to chat bot topics meet a defined threshold relevance standard (each are likely matches), the configured processor may rank the matches as a function of strength of matching likelihood and choose a query topic-chat bot topic match having the highest likelihood to invoke in dialog with the user at the chat bot. Some embodiments may rank or weight said query topic-chat bot topic matches by value of the chat bot topic to the service provider, for example choosing a chat bot topic focused on selling a higher value product over another, or weighting a higher value chat bot topic to increase its likelihood of match value to meet the threshold when it is within a specified tolerance value (for example, adding 10% to its value, so that a 40% likelihood, otherwise determined to be “unlikely” when compared to a 50% likelihood threshold, meets said threshold.
Takiel, para [0007], discloses how each topic persisted in the contextual data structure may be associated with a measure of relevance of the topic to the ongoing human-to-computer dialog. For example, in some implementations, a measure of relevance associated with each topic may be determined based at least in part on a count of turns of the ongoing human-to-computer dialog since the topic was last raised. The more turns since the topic was raised (e.g., added or touched), the lower the measure of relevance for that topic. Suppose a user began a human-to-computer dialog with a question about the weather (causing the topic “weather” to be added), but the dialog then covered a wide range of topics unrelated to weather. The more turns into the dialog since the topic of weather was raised, the more the relevance score associated with the topic weather is diminished. In some implementations, if a topic’s measure of relevance diminishes below a threshold, that topic may be dropped from the contextual data structure altogether.
Conclusion
Other related prior art is listed in the attached PTO-892
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE L THOMAS-HOMESCU whose telephone number is (571)272-0899. The examiner can normally be reached on Mon-Fri 8-6.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 5712727453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2659
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